Big Data-enabled Customer Relationship Management A holistic … · 2018. 6. 24. · Customer Relationship Management (CRM) decisions (Phillips-Wren & Hoskisson, 2015). Despite this

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Information Processing and Management

journal homepage: www.elsevier.com/locate/infoproman

Big Data-enabled Customer Relationship Management: A holisticapproach

Pierluigi Zerbino⁎, Davide Aloini, Riccardo Dulmin, Valeria MininnoDepartment of Energy, Systems, Territory and Construction Engineering, University of Pisa, Largo Lucio Lazzarino 1 56122, Pisa, Italy

A R T I C L E I N F O

Keywords:Big DataCRMLiterature reviewCritical Success Factors (CSFs)Word tree

A B S T R A C T

This paper aims to figure out the potential impact of Big Data (BD) on Critical Success Factors(CSFs) of Customer Relationship Management (CRM). In fact, while some authors have posited arelationship between BD and CRM, literature lacks works that go into the heart of the matter.

Through an extensive up-to-date in-depth literature review about CRM, twenty (20) CSFs weresingled out from 104 selected papers, and organized within an ad-hoc classification framework.The consistency of the classification was checked by means of a content analysis. Evidences werediscussed and linked to the BD literature, and five propositions about how BD could affect CRMCSFs were formalized.

Our results suggest that BD-enabled CRM initiatives could require several changes in thepertinent CSFs. In order to get rid of the hype effect surrounding BD, we suggest to adopt anexplorative approach towards them by defining a mandatory business direction through soundbusiness cases and pilot tests. From a general standpoint, BD could be framed as an enablingfactor of well-known projects, like CRM initiatives, in order to reap the benefits from the newtechnologies by addressing the efforts through already acknowledged management paths.

1. Introduction

Big Data (BD) is considered as a potential enabling factor of business process innovation (Fosso Wamba, Akter, Edwards, Chopin,& Gnanzou, 2015; Loebbecke & Picot, 2015) and as a possible new form of value creation, although the mechanisms of such creationare still unclear (George, Haas, & Pentland, 2014). In fact, these innovations are potentially triggered by the current increased dataavailability in terms of volumes, variety, and velocity, which are data characteristics typically associated with the concept of BD. BDand BD analytics are transforming customer-facing industries (Fosso Wamba, Akter, & Bonicoli, 2013), which are increasingly col-lecting large amounts of customer data, like customers' shopping behaviour, for enabling a real-time decision making (Barton &Court, 2012; Bean & Kiron, 2013; Davenport, Barth, & Bean, 2012). Companies are coping with customer data spread among anincreasing number of data sources, often external or not structured. They are figuring out the potential value of data for generatinginsights on customers, but they are still struggling to integrate the information stemming from the innovative data sources into theCustomer Relationship Management (CRM) decisions (Phillips-Wren & Hoskisson, 2015). Despite this difficulty, some, albeit few,firms have already been able to overcome such a hindrance concretely for CRM purposes: for instance, Sears Holding has beenemploying BD, gathered from several data warehouses related to its brands, for offering more timely, sharp, and granular perso-nalized promotions (McAfee & Brynjolfsson, 2012); Caesars Entertainment utilizes data from its Total Reward loyalty program, real-time play, and web clickstreams for targeting customers with real-time offers through mobile devices, for improving customer

https://doi.org/10.1016/j.ipm.2017.10.005Received 2 January 2017; Received in revised form 8 October 2017; Accepted 11 October 2017

⁎ Corresponding author.E-mail address: [email protected] (P. Zerbino).

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0306-4573/ © 2017 Elsevier Ltd. All rights reserved.

Please cite this article as: Zerbino, P., Information Processing and Management (2017), https://doi.org/10.1016/j.ipm.2017.10.005

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understanding, and for reducing the waiting time for playing for both regular and occasional customers (Davenport & Dyché, 2013).One of the most recent challenges for CRM is to attempt to harness new heterogeneous data sources for developing innovative valuepropositions, for instance by drawing customer data from social networks (Acker, Gröne, Akkad, Pötscher, & Yazbek, 2011; Diffley &McCole, 2015; Faasse, Helms, & Spruit, 2011; Greenberg, 2010; Sigala, 2011; Trainor, Andzulis, Rapp, & Agnihotri, 2014).

Nonetheless, the exploitation of BD sources in firms could require relevant changes of management factors concerning both thepertinent business practices and the Information and Communication Technology (ICT) portfolio (Dutta & Bose, 2015). To our bestknowledge, current scientific literature misses works dealing with how BD could affect development and management of CRM. Inparticular, this work is an endeavour to figure out how BD may reshape Critical Success Factors (CSFs) of CRM initiatives. Despite awealth of knowledge about CRM CSFs, literature still lacks works that investigate the relationship between these factors and apotential BD exploitation. Managers and practitioners might benefit from further insight concerning this relationship: it could clearup the current hype that portrays BD, improving its understanding, and it may provide clues on how to potentially generate com-petitive advantage by means of BD-enabled CRM.

Thus, the objective of this paper is to provide a first answer to the following research question: "How could Big Data affect CriticalSuccess Factors of Customer Relationship Management initiatives from a holistic perspective?". Given this purpose, we developed a clas-sification framework for steering a thorough literature review about CRM CSFs, and for classifying its results. Twenty CSFs wereidentified, and the consistency of the classification was checked through a content analysis. Finally, on the basis of the CSFs, weformulated five propositions about how BD could affect CRM. Both the literature review and the propositions enrich the corre-sponding research streams, and are an attempt to fill up the literature gaps. In addition, they contribute to pave the way for a concreteexploitation of BD, particularly in the CRM context.

The paper consists of five sections: after the introduction, Section 2 presents a critical overview about BD, CRM, and the linkamong them, based on the Resource-Based View; Section 3 describes the methodological steps; Section 4 presents the results from theliterature review and the consistency check, and the propositions about the potential influence of BD on CRM CSFs we derived;finally, conclusions and future developments are in Section 5.

2. Theoretical background

According to the Resource-Based View (RBV), firm's resources are the ultimate antecedent to firm performance (Wernerfelt,1984). Barney (1991), highlights that resources in the RBV (I) are distributed heterogeneously across the firm, and (II) their transferamong firms has always a cost. Despite these assumptions, academics have debated which resources truly enable the achievement ofcompetitive advantage, and different definitions of resource in the RBV have been proposed, e.g. competencies (Prahalad & Hamel,1990), skills (Grant, 1991), physical assets (Litz, 1996), assets (Ross, Beath, & Goodhue, 1996). Wade and Hulland (2004, p. 109)propose a more comprehensive definition, that is, ``resources [are …] assets and capabilities that are available and useful in detecting andresponding to market opportunities or threats'': assets, both tangible and intangible, are everything a firm can use in producing and / oroffering goods or services to a market, while capabilities are repeatable patterns of actions in using the assets (Sanchez, Heene, &Thomas, 1996).

Both knowledge assets (Bharadwaj, 2000) and technology assets, including databases (Ross et al., 1996) and systems formanaging stakeholder relationships (Benjamin & Levinson, 1993), are framed as resources in the RBV (Wade & Hulland, 2004). Thus,it is licit to assume that the current enhanced availability of data could be considered as an improved resource in attaining com-petitive advantage, as well as capabilities for developing knowledge in managing customer relationships.

Accordingly, in the following subsections we present a critical overview about BD, we analyse the different perspective of CRM,and we point out the relationship among the two topics.

2.1. Big Data under a critical lens

The spending guide by the International Data Corporation (IDC, 2015) suggests that the worldwide revenues from BD andbusiness analytics could grow approximately from $122 billion in 2015 to $187 billion in 2019. Large and very large companies willlikely play a major role in this expenditure, which should be mostly accounted for service-related costs. In accordance with this trend,the BD phenomenon seems to be an imperative. Yet, its univocal definition is a still unsolved issue. Several academics rely on theparadigm of the Vs by McAfee and Brynjolfsson (2012), which depicts BD as a data set, akin to a data asset, characterized by (I) ahuge – but not strictly-defined – Volume, (II) Velocity meant as speed of data creation, and / or (III) Variety in the form of data typesand sources. Such a paradigm is evolving with additional Vs (Markus, 2015) like Veracity, intended as quality of data set, or Valuecontained within the data. Nonetheless, literature proposes other approaches in defining BD, as follows.

Wu, Zhu, Wu, and Ding (2014, p. 102) suggest a more narrow but remarkable approach to BD, claiming that its value is embeddedwithin “heterogeneous data types, complex intrinsic semantic associations in data, and complex relationship networks among data”. Thus,any managerial effort to exploit BD should address this multidimensional complexity, and the different Vs might represent an indirectmeasure of this complexity.

Differently, Fosso Wamba et al. (2015, p. 235) frame BD as “a holistic approach to manage, process and analyze 5 Vs (i.e. volume,variety, velocity, veracity and value) in order to create actionable insights for sustained value delivery, measuring performance and estab-lishing competitive advantages”. Therefore, they stress the managerial and business process perspectives, merging them by a

P. Zerbino et al. Information Processing and Management xxx (xxxx) xxx–xxx

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technologic approach for pursuing innovative insight from challenging volumes of structured and unstructured data, and for finallyachieving better off competitive advantage.

Similarly, De Mauro, Greco, and Grimaldi (2016, p. 131) combine different perspectives in defining BD as “the Information assetcharacterised by such a High Volume, Velocity and Variety to require specific Technology and Analytical Methods for its transformation intoValue”. They extend the technologic definitions of BD from a data asset to an information one: this is coherent with the need tocomply with every data quality requirement in order to elicit useful information, and not to simply explore data without a precisebusiness goal.

Diverging from a classical gut-feeling approach, business leaders are increasingly adopting a data-driven decision making, evenwhen the numerical evidences question their past experiences (Lavalle, Lesser, Shockley, Hopkins, & Kruschwitz, 2011). Never-theless, potential benefits from BD adoption are often affected by sensationalism, e.g. companies that have thoroughly taken intoaccount BD challenges within their information strategies by 2015 will surpass unprepared competitors by 20% in all financialmetrics (Beyer, 2011). In line with such considerations, several academics denoted different flaws concerning BD literature, and wearranged them as follows:

1. Vagueness. Current definitions of BD are depicted by hazy concepts (Fox & Do, 2013): Big Data is a poor term (Boyd & Crawford,2012), and even the word Big is misleading (Fox & Do, 2013; George et al., 2014) as it often refers to different volume thresholds.Thence, some academics, e.g. George et al. (2014), claim that the value of BD is in their smartness, that is, the amount of insightextractable from the new data sources and volumes. Because of this vagueness, companies that would like to include BD tech-nologies in their ICT portfolio “are struggling to better understand the concept and therefore capture the business value from `Big Data' ”(Fosso Wamba et al., 2015, p. 234).

2. Lack of managerial focus. There is no clarity on the possible relationship between BD and financial metrics (Fox & Do, 2013), orsimilar outcomes. Besides the shortage of any empirical acknowledgement of this relationship, one reason of the lack of claritycould be that “most Big Data that received popular attention are not the output of instruments designed to produce valid and reliable dataamenable for scientific analysis” (Lazer, Kennedy, King, & Vespignani, 2014, p. 1204). Consequently, current assessments aboutpotential returns from BD investments might be unreliable and biased, or premature at least, and further research is definitelyneeded.

3. Trivial significance. The increasing trend of data volume, variety, and velocity has always led the evolution of the ICTs, and itshould not be considered as something unexpected. What is really changing is its pace, whose intensity has generated newmanagement and data exploitation problems. The main issue is that usual statistical approaches, which rely on p-values to assessthe significance of an outcome, become ineffective because of the huge volumes of data: due to noise accumulation, incidentalendogeneity, or spurious correlations (Fan, Han, & Liu, 2014), almost everything possible phenomenon turns out to be significant(George et al., 2014). Furthermore, the data volumes we are trying to harness today “have higher possibility of different inter-pretations [… and] need a more explorative and experimental approach to yield value (if any)” (Jukić, Sharma, Nestorov, & Jukić,2015, p. 203).

These three flaws could foster the fashion effect that affects BD, limiting the clarity about the potential role of BD and likelyyielding a detrimental effect of firms' choices. In fact, the decision to proceed or not in an investment in innovative ICTs is oftenaffected by the hype surrounding both the new technologies (Fox & Do, 2013) and the expectations towards them (Light & McGrath,2010; Thompson, 2011). Managers have to strive to overtake the hype by assessing the capabilities of the new technology minimizingpossible distortions and bias (Collins, Worthington, Reyes, & Romero, 2010; Zane & Reyes, 2010), and BD is no exception.

In order to align managerial efforts and BD exploitation, we deem that purely technologic approaches to BD may be misleading.Undoubtedly, BD involves the use of technologies but, if a firm aims to rely on an advanced data-driven decision making, not only ona strategic level but also on a tactical and operational one, a holistic approach to the generation, collection, and harnessing of the newdata sources could be more suitable. Thus, in our opinion, the BD definition by Fosso Wamba and colleagues may be the mostappropriate for attempting to adopt a more objective and managerial overture to BD initiatives, and for providing guidance indeveloping this paper.

2.2. The different perspectives of Customer Relationship Management

CRM was commonly contextualized within technology solutions and was described as akin to an information-enabled form ofrelationship marketing (Ryals & Payne, 2001). Fayerman (2002), distinguishes (I) Operational CRM, which manages current cus-tomers' interactions, (II) Analytical CRM, which fosters decision-making by analysing and re-arranging customer data, and (III)Collaborative CRM, which aims to an improved customer experience by leveraging inter-departmental teamwork and communicationwithin a firm. Greenberg (2003) distances himself from pure technological approaches, and propose that ``CRM is a philosophy and abusiness strategy supported by a system and a technology designed to improve human interactions in a business environment''.

Scientific literature has suggested further different interpretations of CRM's nature. On the basis of an extensive review, Zablah,Bellenger, and Johnston (2004a) define five major perspectives on CRM:


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• CRM as a process. CRM is a macro-process that encompasses all the activities for pursuing a long-term, profitable, and mutuallybeneficial customer relationships; from a narrower perspective, it is a process limited to the management of customer interactionsto establish and maintain durable worthwhile relationships.

• CRM as a strategy. Firms should design and prioritize the investment of resources on relationship building and maintenance in linewith the customer's lifetime value.

• CRM as a philosophy. Customer loyalty, and thus profitability, requires a continuous understanding of customers' evolving needsfor the best value delivering.

• CRM as a capability. The potential, additional competitive advantage that CRM can provide is tied to the capacity of gatheringknowledge on current and prospective customers, and to act upon it, for instance by proactively reshaping customer interactions.

• CRM as a technology. Technologies for managing knowledge and interaction, linking front- and back-office functions, play a non-negligible role in firms' relationship management efforts.

Building on the insight stemming from these five perspectives, Zablah et al. (2004a) contend that the process approach is the mostcomprehensive one, and they conceptualize CRM as “an ongoing process that involves the development and leveraging of market in-telligence for the purpose of building and maintaining a profit-maximizing portfolio of customer relationships” (p. 480). Within this processview, CRM technological tools are part of the resources in input to the CRM process.

Payne and Frow (2005) adopt three different CRM perspectives, which can be depicted as a continuum. On one side, CRM can bedefined (perspective 1) narrowly and tactically as the implementation of a specific technology solution, for instance a Sales ForceAutomation project. On the other opposite side, CRM is defined (perspective 3) broadly and strategically as a holistic approach to aprioritized management of customer relationships for creating shareholder value. Between these two standpoints, CRM can also beframed (perspective 2) as a wide range of integrated customer-oriented technology solutions.

Payne and Frow bolster that organisations should conceptualize CRM as a strategy (perspective 3) whose purpose is to achieveshareholder value by building up and maintaining a profitable, long-term relationship with key customers, notable customer seg-ments and, in a broad meaning, key stakeholders. Through several kinds of data and data sources, CRM aims to create value togetherwith the customers, merging relationship marketing strategies and Information Technology (IT) in a cross-functional integration ofprocesses, operations, and human resources. Yet, the definition by Zablah and colleagues is sharper, and it better defines the role oftechnology and knowledge in leveraging the management of customer relationships. Thus, we preferred it for the further develop-ment of our work.

2.3. The relationship between Big Data and Customer Relationship Management

The link between BD and the CRM process has already been hypothesized in literature and, in order to explain it, we took intoconsideration the CRM process by Reinartz, Krafft, and Hoyer (2004). By stressing the relevance of focusing on a single view of thecustomer, coordinating information during time in a multi-channel perspective, Reinartz and colleagues suggest that the CRM processhas three primary dimensions – relationship initiation, maintenance, and termination – divided in a total of nine subdimensions: asshown in Fig. 1, some applications of BD, prospective or already developed, could be applied to most of them.

BD allows firms to develop advanced segmentation, e.g.micro-segmentations in real time (Fosso Wamba et al., 2015), which could

Fig. 1. Potential impact of BD on CRM subdimensions.


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be exploited to refine all the CRM activities. Clustering and classification techniques in BD environment present new specific chal-lenges, but may help to identify groups of customers that react to the same market stimuli and to single out fine-grained profiles ofindividual customers (Fan, Lau, & Zhao, 2015). The development of BD-enabled advanced segmentation may draw from customer'sbehaviour, like experiences, sentiments, or attitudes, collected from behavioural data sources or inferred by means of advancedanalytics (Kumar, 2015): in this regard, machine learning has shown excellent performances in BD-enabled customer segmentation(Sundsøy, Bjelland, Iqbal, Pentland, & De Montjoye, 2014). BD in CRM includes more specific activities, like analysis of the wholepath that a customer follows from the initiation to the termination of its relationship with the firm (Dietrich, Plachy, & Norton, 2014),proactive customer retention exploiting predictive churn modelling (Kumar, 2015), foresight of signs of customer dissatisfaction thatmight lead to attrition (Davenport, 2014), exploitation of wider log data for more tailored recommender systems (Fan et al., 2015).

The adoption of BD in CRM initiatives is not limited to the utilization of huge volumes of structured data. In fact, the link betweenBD and CRM has been evolving and strengthening with the introduction of social media, which enable a better off collection of semi-and un-structured data for developing insight on prospects and current customers, on-going market trends, and possible unforeseenpatterns among data (Torre-Bastida, Villar-Rodriguez, Gil-Lopez, & Del Ser, 2015). Mohan, Choi, and Min (2008), introduced theSocial CRM concept, also known as CRM 2.0, described as the combination of the Web 2.0 paradigm and social networking with theCRM systems. Social CRM implies bidirectional dynamic interactions with customers by means of different online channels. Theinnovative insight obtainable from these interactions is “based on customer data, customer personal profiles on the web and the socialcharacteristics associated with them, and customer participation in the activity” (Greenberg, 2010, p. 414). Social CRM is not applicable ifcustomer management using CRM has not been already developed in the firm (Faasse et al., 2011), and it definitely benefits from BD:for instance, for commercial recommendations, automated categorisation and routing of customer interactions, predictive models oftrend and clustering of customers (Orenga‑Roglá & Chalmeta, 2016), advanced segmentation, monitoring of customers' portfolio(Marshall, Mueck, & Shockley, 2015).

According to the definitions we chose for BD and CRM, the utilization of BD in CRM consists in applying a holistic approach inmanaging a process whose inputs include ICTs.

3. Objective and methodology

The goal of this paper is to provide a first answer to the following research question: “How could Big Data affect Critical SuccessFactors of Customer Relationship Management initiatives from a holistic perspective?”.

In line with the research question, we followed the methodology in Fig. 2: the squared rectangles are the four main steps, whilethe dotted rounded rectangles are the main outputs. First, we structured an ad-hoc classification framework, consisting of five macro-categories, for leading the identification of the most critical areas in CRM. Second, a multi-staged in-depth literature review aboutCRM was performed, allowing the extraction of twenty CSFs from 104 selected papers, and the CSFs were discussed and arranged onthe basis of the classification framework we developed. Third, the consistency between the selected papers and the formulation of themacro-categories which they were assigned to was positively assessed by building, analysing, and discussing ten word-trees – a formof content analysis – through NVivo 11 Pro. Fourth, in coherence with the outcomes of the previous steps, five theoretical propo-sitions concerning the potential effect of BD on the twenty CRM CSFs were critically formulated.

The following sub-sections detail the four-step methodology we adopted: building of the classification framework, development ofthe literature review, content analysis, and formulation of the propositions.

3.1. Classification framework

In order to set a theoretical foundation for singling out CRM CSFs in the literature, we structured an ad-hoc review and classi-fication framework. As a review should be concept-centric (Webster & Watson, 2002), the concept leading the building of ourframework was the identification of the critical areas of CRM, with the purpose of addressing the elicitation of the CSFs in developingand managing CRM initiatives. The framework consists of five macro-categories – strategy, technology, data and insight, project, andorganization – and it was developed drawing upon the five CRM perspectives depicted by Zablah et al. (2004a).

The categories were used as main concepts for leading the subsequent literature review, similarly to the concept matrix suggestedby Webster and Watson (2002), and for classifying its results. As follows, we describe the rationale underpinning the framework.

While contending that the process perspective is the most suitable one for conceptualizing the CRM phenomenon, Zablah et al.(2004a) claim that the other perspectives they propose provide useful insights in refining the nature of the CRM process, and indefining its inputs and outputs. The CRM's intended purpose is the rational use of limited resources for enabling the building of a

Fig. 2. Methodology.


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profit-maximizing portfolio of customer relationships. This output is attainable through appropriate inputs, which are represented bythe other four CRM perspectives.

1. The strategic perspective aims to steer CRM to a greater organisational profitability by keeping into account both customer's needsand firm's capabilities. The allocation of resources for relationship building and maintenance must be consistent customers'lifetime value (Ryals, 2003) and with the type of customer, which could also lead to the decision of not to establish any kind ofrelationship (Verhoef & Donkers, 2001).

2. The philosophical perspective connotes the need for customer centricity as one of the pivotal organizational resources for CRM.Organizational change is inevitable in CRM initiatives, and it involves new processes and procedures to be implemented, as well asbehavioural changes in employees (Shum, Bove, & Auh, 2008), company-wide cross-functional Business Process Reengineering(BPR) (Chen & Popovich, 2003), and changes in the organizational structure (Beldi, Cheffi, & Dey, 2010).

3. The technological perspective suggests that CRM tools are a fundamental input to the CRM process to improve building, dis-semination, and application of customer intelligence across different touch-points. In fact, CRM success seems to be heavilyinfluenced by the alignment between technology, on one side, and processes and employees, on the other side (Chen & Popovich,2003; Zablah, Bellenger, & Johnston, 2004b).

4. The capability perspective proposes that firms have to possess a set of synergistic resources – that is, capabilities – for enablingfirms to develop knowledge on customers and prospects, and to act upon it. Accordingly, Zablah et al. (2004a) include KnowledgeManagement (KM) as a major sub-process of the CRM macro-process, and they define it as the set of activities for creating andleveraging the market intelligence towards building and management of a profit-maximizing portfolio of customer relationships.In order to pinpoint the pertinent information management activities, firms should utilize appropriate IT tools and applications(Payne & Frow, 2005).

The CRM approach by Zablah et al. (2004a) entails, obviously, the formal adoption of a CRM process by the firm. CRM initiativesusually require the development of a CRM project upstream, which can be really challenging, with a failure rate between 35 and 75%(Zablah et al., 2004b). Successful CRM initiatives rely on employee engagement (Payne & Frow, 2005) and on efficient project andchange management (Bygstad, 2003; Mendoza, Marius, Pérez, & Grimán, 2007), and a project perspective on CRM could be a key tosuccess (Beldi et al., 2010). Thus, we claim that a project standpoint should be considered in our framework.

In light of the above considerations, Fig. 3 summarises the theoretical foundation for defining the macro-categories of the reviewand classification framework. The project perspective and its arrow are dotted in order to highlight that they intervene upstream indeveloping the CRM process only.

According to Fig. 3, we propose a possible framework, consisting of five categories, for eliciting and classifying the CSFs of CRMinitiatives. The perspective which each category stems from is highlighted between round brackets.

1. Strategic planning of CRM (Strategic perspective). CRM needs for a strategic orientation that should be framed within the com-pany's overall strategies. Its mid- and long-term impacts may imply relevant effects that should be carefully evaluated.

2. Infrastructure for IT in CRM (Technological perspective). A diversified ICT portfolio should be able to support the CRM process withappropriate hardware and software resources. Then, the pertinent requirements are fundamental for a smooth value co-creationwith the customer.

3. Insight and data management in CRM (Capability perspective). Effective CRM should be based upon the benefits from a usefulgathering and exploitation of both customer data and upon all the insight extractable from them. Thus, data should be specificallyarranged and managed within a formal process, and these activities should be supported by means of suitable technologies.

4. CRM project (Project perspective). A project management approach may facilitate the formal institutionalization of a CRM processwithin a firm. Whether a CRM initiative includes the implementation of a CRM system, the potential hindrances to success maybecome tougher because of the introduction of the pervasive new Information System (IS) and, then, a warier approach is needed.

Fig. 3. Theoretical basis for the review and classification framework.


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5. CRM and organizational impact (Philosophical perspective). CRM exerts a strong impact on both other business processes and theorganizational structure. With the purpose to steer the full exploitation of the potential benefits linked to the value co-creationwith the customers, a critical redesign of the information and process flows and of the interfaces that link business processes andthat connect employees / teams could be fundamental.

3.2. Literature review

By taking into account the review principles by Webster and Watson (2002), a multi-staged in-depth literature review foridentifying the CSFs in developing and managing CRM initiatives was carried out in six main steps: (I) identification of keywords andsearch strings; (II) definition of the time window; (III) selection of the citation database(s); (IV) selection of the disciplines / topics;(V) refinement of the results through a multi-staged skimming process; (VI) review of the relevant papers.

Table 1 shows the protocol that led the first four steps of our literature review. The exclusion of CSFs from the keywords allowedus to take into consideration a wider set papers for the identification of the factors.

The fifth step of the review – refinement of the results through a multi-staged skimming process – was decomposed in three phases(Table 2). First, title, abstract, and keywords of each paper were carefully read. Conference papers were further skimmed by con-sidering only those belonging to the Association of Information Systems, which are known for their quality: ICIS, ECIS, AMCIS, andACIS. Second, introduction, objectives, methodology, and results of the papers that passed the first selection were examined. The twofirst skimming phases led to a restricted pool of 232 journal papers and 6 conference papers, which were clustered in line with theyear of publication, and prioritized according to citations and impact factor. Finally, in the third skimming phase, the full-body of theretained papers was deeply analysed, leading to the final pool of papers.

Thus, the authors were randomly divided into two couples: each couple independently identified the CSFs within the retainedpapers, and univocally assigned them to the appropriate category of the classification framework. Finally, the results were comparedand critically discussed until convergence.

Table 1Protocol for the literature review.

PARAMETER DETAIL JUSTIFICATION

KEYWORDS "Customer Relationship Management" in Title, Abstract, andKeywords OR CRM in Title OR CRM in Keywords.

In order to provide the widest possible results, we preferred to explorethe whole CRM topic and to proceed with a reasoned extraction ofexplicit and implicit CSFs from the papers. Thus, although the purposeof the review was to identify the CSFs, the keyword CSF was notconsidered.

TIME WINDOW Jan 2003 ÷ Feb 2016 Logical continuity with the CRM review by Ngai (2005), whose timewindow is 1992–2002.

TYPE OF ARTICLE Journal (J), reviews, Conference (C) Journals are recognized as the best scientific source; according toWebster and Watson (2002), selected conference proceedings should beexamined, especially those with a reputation for quality.

SOURCES Scopus Elsevier Scopus is recognized as the largest abstract and citationdatabase of peer-reviewed literature. In addition, it was preferred to ISIWeb of Science (WoS) for its better flexibility. All the main results of thequery were found on WoS too, through similar queries.

DISCIPLINES Business, Management and Accounting; Computer Science,Engineering; Social Sciences; Decision Sciences; Economics,Econometrics and Finance.

Disciplines were selected according to their fit with the researchobjective.

Results of the query J papers: 2459; C papers: 1811

Table 2Phases for skimming the papers.

SKIMMING PHASE CRITERIA FOR FIT EVALUATION NUMBER OF RETAINED PAPERS PER STEP

PHASE 1 J: title, abstract, and keywords; C: belonging to the selected conferences (constraint);title, abstract, and keywords.

J: 444 out of 2459 (18%)C: 29 out of 1811 (16%)

PHASE 2 J and C: introduction, objectives, methodology, results. J: 232 out of 444 (52%)C: 6 out of 29 (21%)

PHASE 3 J and C: full-body of the paper. J: 104 out of 232 (45%)C: 1 out of 6 (17%)


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3.3. Content analysis

After the CSFs classification, the coherence between the selected papers and the five macro-categories was assessed throughNVivo 11 Pro, a software package for content analysis,1 by building and analysing word trees. A word tree is a visualization andinformation-retrieval technique belonging to the wider category of Keyword-In-Context techniques, and it “enables rapid querying andexploitation of bodies of text” (Wattenberg & Viégas, 2008, p. 1221). Keyword-In-Context techniques are known for facilitating thedevelopment of categories (Krippendorff, 2004), which is our case.

The word trees were developed by singling out and analysing the co-occurrences of specific keywords, chosen jointly by theauthors according to the definition of the five CRM macro-categories, within the selected papers. These co-occurrences can witness alogical alignment between the text of the retained works, which the CSFs were extracted from, and the conceptualization of the CRMcategories, which the CSFs were assigned to. If such an alignment exists within the body of papers, the coherence check may beappreciably positive, strengthening the link between the papers and the classification framework.

Within the NVivo environment, the word tree function explores how specific keywords develop within a text. Its output is a wordtree, that is, a graphical outcome that recalls the shape of a tree: the top of the tree is the keyword, and the branches and the leavesare those parts of the in-text sentences that precede or follow the keyword. This function requires two inputs: text and keywords. Textis the textual object to analyse, that is, the selected papers, and it must be arranged in one or more logical nodes. A logical node is acluster which the text should be assigned to. We created five logical nodes, one for each macro-category, and we assigned the papersaccording to the Table A.1 in Appendix A, which contains the references of our review for each macro-category. A keyword is a wordthat the software can find and analyse within the text. By combining the keywords through the Boolean logic, the software canhighlight their co-occurrences in the body of the text if they are distant each other no more than n words: then, n is a proximitythreshold, and it can be set by the user.

Table 3 explains the protocol we developed for the word tree analysis.

3.4. Formulation of the propositions

Key evidences stemming from the previous steps were analysed in light of the existing BD literature. Thus, with the purpose ofexplaining the possible impact of BD on CRM CSFs, five theoretical propositions were formulated and discussed. For a matter of

Table 3Protocol for the word tree analysis.

PARAMETER DETAIL JUSTIFICATION

KEYWORDS • Strategic planning of CRM: strategy, needs (requirements);• Infrastructure for IT in CRM: technology, value (performance);• Insight and data management in CRM: knowledge (insight),integration;• CRM project: management, resources;• CRM and organizational impact: structure, processes (interface);

Since the number papers in input is high, and in order to obtainmore compact, manageable, and understandable results, wechose no more than two keywords for each logical node. Theywere formulated in coherence with the definition of the fivemacro-categories. Keywords between round brackets weredropped and replaced due to their low frequency within thepapers, or because they led to unclear results.Each couple of keywords was combined through the ANDBoolean operator with the purpose of investigating theircoupled occurrence.

PAPERS IN INPUT See Table A.1 in Appendix A The selected papers for each CSF are the outcome of theliterature review.

PARTS LEFT OUT • Title, abstract, keywords;• References;• Appendices;

Some parts of the papers were excluded from the query in orderto focus only on the body of the text, and to find only the mostmeaningful relationships among the keywords.

• Footnotes and headnotes;• Tables combining numbers and keywords;

PROXIMITY THRESHOLD Thirty (30) words The threshold was chosen for trying to consider those cases inwhich the two keywords occur separately in consecutivesentences. Thirty was considered as adequate because, usually,scientific papers do not include very long sentences.

CRITERIA FOR REDUCING

REDUNDANCIES

We excluded the occurrences of the keywords:• Between square brackets;• Within bullet lists, if alone or in short sentences;• Preceded or followed by numbers or references;• Used with other meanings, for instance needs as a verb and notas a noun;

Given the huge amount of text in input, the resulting word treescould be too much big, and not easily understandable, if nottrimmed. In order to facilitate their representation andinterpretation, they were rationalized according to these fourcriteria, which were chosen for keeping those branches that canconvey the most understandable meaning.

1 See www.qsrinternational.com/what-is-nvivo for further information.


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http://www.qsrinternational.com/what-is-nvivo

logical development of the paper, the number of the propositions was aligned to the number of the CRM macro-categories, which inturn stemmed from the five CRM inputs, and their formulation was derived from a critical analysis of the relationship between eachCSF and BD literature.

In order to derive the propositions from the literature review, the authors were randomly divided in two couples, avoiding thesame coupling of the step 2. Each couple independently reviewed the evidences concerning the twenty CSFs, and sketched out their

Fig. 4. The identified CRM CSFs organized within the classification framework.

Fig. 5. Number and temporal distribution of the selected journal papers.


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possible link with BD literature. Results from the two teams were compared and refined until convergence, and the five propositionswere jointly developed and discussed until reaching consensus.

4. Results and discussion

In the first part of this section, we expound the results of the literature review and their classification within the framework. In thesecond part, we describe the outcome of the content analysis. Finally, we derive the propositions about the potential impact of BD onCRM CSFs.

4.1. Results from the literature review

Twenty (20) CSFs were singled out from the 104 journal papers and assigned to the most pertinent macro-category of theclassification framework (Fig. 4) on the basis of the fit between their definition and the formulation provided for each category. Allthe references for each CSF are in Table A.1 in Appendix A. Fig. 5 shows the temporal distribution of the body of the selected papers.

Table 4 describes the results of the review in a matrix structure (Cerchione & Esposito, 2017; Goodhue & Thompson, 1995): itsrows identify the CSFs arranged in the same order as in Fig. 4; the first column provides a conceptualisation for each CSF; instead, thesecond column describes the main evidences for each success factor from the retained papers.

As an additional result, our review shows that scientific literature has neglected, and maybe underestimated, the relationshipbetween BD and CSFs of CRM and of other business processes. In fact, although the BD concept in its current meaning has started tospread some years ago (see Jacobs, 2009), only two papers (i.e.Malthouse, Haenlein, Skiera, Wege, & Zhang, 2013; Woodcock, Green,& Starkey, 2011) link CRM CSFs to the possible exploitation un-structured and semi-structured data for value co-creation, specificallyin terms of social media data, whilst the explicit relationship between CSFs of classical CRM and BD was not considered. The reasonmay be twofold: on one side, the hype effect may have disguised BD as a mandatory investment, regardless of the additionalcriticalities it may add to the business processes; on the other side, to our best knowledge, most papers hypothesize several benefitsand constraints concerning BD, but very few tries to explore the risks linked to such initiatives.

4.2. Outcome of the content analysis

The output of the analysis consisted of ten word-trees (see figures from Fig. B.1–B.10 in Appendix B), one for each keyword. Figs.B.1–B.10 show their trimmed version, for a matter of both compactness and understandability. Given a main keyword in the middleof a word tree, blue underlined terms are the nearest occurrences of the second corresponding keyword that, according to theprotocol in Table 3, refers to the same logical node: for instance, Fig. B.1 concerns the keyword Strategy belonging to the Strategicplanning of CRM logical node, and the blue word is Needs, which is the second keyword belonging to the same node. Instead, blackunderlined words are the nearest occurrences of the same keyword, e.g. Fig. B.2 is focused on the keyword Needs, and the same wordoccurs two times more in the same word tree. All the keywords have numerous branches, which means that their frequency within thecorresponding papers is high. In detail, we discuss the results for each macro-category, as follows:

• Strategic planning of CRM (Figs. B.1 and B.2). This is the only case in which the co-occurrence of the two keywords does not show ahigh frequency within the proximity threshold. This is unexpected because the frequency of the two separate keywords does notseem low, and strategy and needs should be naturally linked because, usually, customer needs play a central role in CRM strategies(Fig. B.2). The reason for their distance within the papers may be that the selected scientific papers consider this link implicitly,and, according to Fig. B.1, they concern more focused aspects like the need of a cross-functional approach, information sharing,multi-channel strategy, integration, and so on, which are in line with the need for a long-term strategic orientation.

• Infrastructure for IT in CRM (Figs. B.3 and B.4). The branches show a strong logical relationship between technology and superiorvalue creation for the customer and for the firm (Fig. B.4). Even though the words hardware and software are part of the definitionof this macro-category, they do not occur in Fig. B.3. Despite this, the term infrastructure is quite recurrent within the papers, andit covers both of them, implicitly.

• Insight and data management in CRM (Figs. B.5 and B.6). The two word-trees point out a sound link between knowledge andintegration in the papers. In line with the definition of the macro-category, integration is meant as “specific arrangements andmanagement” of the data, which knowledge should be extracted from.

• CRM project. Given their general nature, management and resources show a huge frequency that led to numerous branches (Figs.B.7 and B.8). Yet, Fig. B.7 suggests that CRM requires a formal management of several aspects that are part of the projectmanagement: resource management, project championship, support from top management, involvement. In addition, the het-erogeneity of the resources involved in CRM, like human, financial, tangible, intangible (Fig. B.8), is consistent with a holisticmanagement of the CRM project complexity.

• CRM and organizational impact. Figs. B.9 and B.10 contain explicit statements about the need for changes in both business pro-cesses and organizational structure in order to challenge a CRM initiative, or to pursue the customer orientation.


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Table 4Definition and discussion of the CRM CSFs.


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Globally, all the ten word-trees show that the content of the retained papers is consistent with the definition of the pertinentmacro-categories. Therefore, it is licit to assume that the coherence check was positive, enhancing the soundness of both the literaturereview and the classification within the framework.

4.3. Big Data-enabled Customer Relationship Management

Drawing upon the evidences from the literature review and the content analysis, we formally elicited five propositions aboutthe potential impact of a BD approach on the five categories of CRM CSFs. The propositions were developed by addressingthe relationship with the corresponding CSFs, which were highlighted by their progressive number (see Fig. 4) between squarebrackets.

4.3.1. Strategic planning of Customer Relationship ManagementA successful CRM initiative, aiming to value co-creation with customers [#2], requires a multi-channel holistic approach that

should be integrated with the overall corporate strategy, and that should fit the market and geographical context [#4]. E-commerce systems collect less structured data (Chen, Chiang, & Storey, 2012) that, in the last few years, have become a com-petitive variable which cannot be neglected within a CRM strategy. Yet, we deem that any decision to harness BD sources shouldneither pertain to circumstances nor be occasional: a sound comprehension of the benefits and of what it is possible to achieve ornot by means of BD in CRM must be embedded within the overall marketing strategy [#1]. This strategy design is mandatory inorder to ascertain the right and reasonable expectations, and to clear out myths about what BD could allow to achieve. In fact, adecision-making based only upon data might be a nonsense (Harford, 2014): efforts in doing analytics without a strategic businessdirection are likely to stall, and the inevitable outcomes could be squandered resources, and scepticism about the investment(Lavalle et al., 2011).

In addition, managers should strive to assess BD initiatives, and their usefulness, by setting appropriate metrics [#3] (Phillips-Wren, Iyer, Kulkarni, & Ariyachandra, 2015). The reason underpinning this requirement is that current measurement limitationsentail difficulties in distinguishing returns to emerging data technologies from returns to traditional database systems (Tambe, 2014).

Accordingly, we state:Proposition 1. “CRM strategies should be enriched with a BD-led business direction, if applicable and relevant to the addressed market, inorder to bridle the new data”.

4.3.2. Infrastructure for IT in Customer Relationship ManagementOur literature review points out that technological readiness and integration among different ISs are two of the most widespread IT

requirements in CRM implementations. In fact, IT is an acknowledged enabler of CRM processes, and this includes BD technologies.Thus, any BD-enabled initiative that aims to extract additional value from new data sources should not neglect [#5]:

• Systems integration; advanced visualization functions for singling out patterns within BD databases (Dutta & Bose, 2015);

• Coexistence of BD solutions with legacy analytics and warehousing technologies (Davenport & Dyché, 2013), which should bekept, and not removed, for supporting and fuelling the new ones (Lavalle et al., 2011);

• Check for issues like local optimums, transmission costs, and privacy concerns in aggregating distributed data sources for acentralized mining (Wu et al., 2014);

Then, we formulate:Proposition 2. “BD-enabled CRM should rely on a synergy between the additional BD technologies, with advanced data visualisationcapabilities, and the existing analytics and data warehouses”.

4.3.3. Insight and data management in Customer Relationship ManagementLike every data intensive process, CRM requires both integration / centralization of all the customer data into a golden record,

that is, a single and univocal database, and appropriate collection, management, and exploitation of all the profitable informationpulled out from such database. Nonetheless, an important caveat is that semi-structured and unstructured data, like those fromsocial media, could definitely worsen data quality, and this could jeopardize the Veracity of the 5 Vs paradigm. Moreover,“currently there is no acknowledged and efficient data model to handle Big Data” since traditional data models are not able to managethe increased complexity (Wu et al., 2014). Data should be managed in order to pursue an alignment between the structure of thedata source and the form of the new obtainable insight, privileging an effective visualisation for fostering information diffusionwithin the firm.

In line with Propositions 1 and 2, we state [#6, 7]:Proposition 3. “Traditional customer data warehouses must fuel and be integrated with emergent BD warehousing technologies”.

4.3.4. Customer Relationship Management projectThe decision to embed a BD initiative in a traditional CRM project may increase the overall management complexity because


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of severe issues in evaluating the potential benefits and the competitive value of the enhanced data availability, from both atangible and intangible point of view. In order to mitigate these problems, an initial proof of concept for investigating, eliciting,and illustrating the benefits of the BD involvement before applying the new solutions to the current or reengineered businessprocesses (Davenport & Dyché, 2013) may be a wary and necessary approach [#10]. The proof of concept should be part of awider business case, which has to include structured statements about how BD should or could be harnessed. This suggestion iscoherent with the necessity of designing and developing a pilot test of the important strategic assumptions in CRM projects,prompted by Curry and Kkolou (2004). Such tests should be performed by a cross-functional supporting team, which will likelyuphold the whole BD-enabled CRM project, and that should possibly include data modelers and scientists [#13, 14] (Dutta &Bose, 2015).

Since an interdepartmental critical review of customer needs within a firm is relevant to CRM success (Chang, 2007), in ouropinion the diffusion of the BD solutions should be fostered among all the departments of the company that wants to implement thenew technologies [#12], and the needed project champion [#8] should endorse and facilitate this process. So, in line with Dutta andBose (2015), we deem that stimulating prospective user acceptance towards the BD solutions is doubtlessly important, and it shouldbe planned among the upstream CRM activities [#11].

In addition, firms that aim to deploy CRM initiatives that involve BD will struggle to attract and acquire the appropriate skills totake advantage of the BD tools, for instance advanced skills in extracting, structuring, and modelling data [#14]. BD scientists shouldalso possess visual and verbal skills for explaining BD outcomes to executives (Davenport & Dyché, 2013), and a background inmarketing (Leeflang, Verhoef, Dahlström, & Freundt, 2014) for supporting the development of successful fact-based marketingpropositions (Verhoef & Lemon, 2013).

Thus, we state:Proposition 4. “BD-enabled CRM projects have to include a business case and pilot tests for dissipating the uncertainty around concreteobjectives and potential benefits of the initiative”.

4.3.5. Customer Relationship Management and organizational impactCRM needs for an organizational culture that is fit for the customer orientation. The decision to rely also on BD sources and tools

in CRM requires further changes in both culture and incentive system. In fact, employees at all the levels in the firm should shift to adata-driven approach for harnessing the new data sources [#9, 17], and they should be incentivized to accept and to utilize the newtools [#19] (Dutta & Bose, 2015). These requirements are needed for achieving the best consistency with the way BD will be used inredefining the value propositions, and to obtain the desired benefits. Consequently, the typical BPR for CRM should be orientedtowards an intensive use of BD analytics [#16] (Dutta & Bose, 2015; Tambe, 2014). The re-engineered processes should show asatisfactory fit with the already existing technologies and the new ones [#15], and previous experience in CRM initiatives [#20]could help in attaining the best fit.

A BD-oriented change management program should frame and underpin the required organizational and process redesign. Forinstance, according to the well-known ADKAR framework for change management, people involved in BD-enabled CRM should be:informed (Awareness) about the benefits of the initiative and all the other contents of the business case; motivated (Desire) to beinvolved in the change to a data-driven approach to CRM; skilled (Knowledge and Ability) for coping with the change starting fromthe “old” gut-feeling decision making; and active (Reinforcement) in supporting and strengthening the change. With the purpose ofpursuing the needed holistic customer orientation, change management activities have to include a worthwhile inter-functionalcommunication, collaboration, and integration [#18] that should continue and evolve even beyond the end of the CRM project,matching the overall CRM strategic and operational needs.

In order to cope with the heterogeneity of the data sources, it could be preferable to appoint a single leader that should beresponsible for building up and managing all the analytic capabilities, in example a Chief Analytics Officer or a similar role [#17]. Heshould be supported by a staff that should encompass both conventional quantitative analysts and data scientists (Davenport &Dyché, 2013).

Therefore, we state:Proposition 5. “Organizational and process redesign in BD-enabled CRM should aim to a pervasive management and exploitation of all thekinds of data across the firm. The continuous inter-functional dialogue in a CRM perspective must facilitate the required change managementand the attainment of the best fit between old and new technologies and the reengineered processes”.

5. Conclusions

This work is a first answer the research question “How could Big Data affect Critical Success Factors of Customer RelationshipManagement initiatives from a holistic perspective?”. In the following sub-sections, we discuss scientific and managerial implications ofour findings, limitations of this work, and suggestions for potential future research.


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5.1. Scholarly implications

From a scientific standpoint, this is a first attempt to explore the consistency between BD and CRM initiatives, a topic that iscurrently underestimated and underexplored. On the basis of the four methodological steps, we deem that the exploitation of BD inCRM may not imply the introduction of new CSFs, but it will likely affect both formulation and management of the known ones, ashypothesized in the five propositions.

This work provides three contributions. First, scientific literature could benefit from our in-depth up-to-date review about CRMCSFs: past reviews (e.g. Croteau & Li, 2003; Mendoza et al., 2007) are still valuable contributions, obviously, but they could lack themore comprehensive point of view stemming from over 10 additional years of academic research. Furthermore, to our bestknowledge, no other review analysed a number of papers comparable to the body of works we considered. Second, the five pro-positions we formulated formally integrate the CRM and BD streams. In fact, although such streams offer a wealth of works, literaturestill lacks contributions dealing with how BD and CRM might interplay directly, trying to strip these opportunities of the fashioneffect that imbue them: almost all the contributions that address the BD topic from a non-technical perspective (e.g. Fan et al., 2015;Rust & Huang, 2014) are not focused on CRM, and they only provide very limited insights about it. Third, in line with the previouscontributions, and by building upon the process view of CRM, this paper lays the foundations for a new roadmap of research in CRM,that is, the BD-enabled CRM.

By contextualizing BD in the CRM process view we adopted, BD should not be considered as a completely new paradigm, but as aholistic management approach to CRM – a point of view that is consistent with the more general perspective on BD by Fosso Wambaet al. (2015) – and its new technologies should be stripped of the fashion effect and should be objectively treated as innovative ICTs.Extending such considerations to a broader point of view, ICTs are a well-known enabling factor of other several relevant processes orprojects, besides CRM. Consequently, by drawing upon the wide body of knowledge about such projects, and by framing BD as anenabling factor of these initiatives, it could be possible to understand how to concretely harness the potential of BD within a di-versified ICT portfolio in an explorative way, and how its holistic impact could affect business practises with customers in a firm.

5.2. Managerial implications

From a practitioner perspective, by drawing upon those enabling factors that CRM and BD could likely share, the answer to ourresearch question might pinpoint a potential way to harness BD, that is, the BD-enabled CRM. Unfortunately, the BD environmentmay be still surrounded by hype, and this could hamper identification and achievement of potential benefits stemming from BDinitiatives. In this perspective, investments in pilot tests within the business processes of the firm may be even more valuable, for tworeasons: for figuring out the needed changes, according to our propositions; and for taking distance from the fashion effect of BD bycontextualizing these initiatives in a more tangible environment, clarifying the BD real potential. The decision to manage a BD-oriented initiative could make sense only if there is an unambiguous and holistic business direction underpinning it. Without it, theproject will likely result in an escalation that can multiply doubts and uncertainties about such initiatives, and that will wasteresources. Therefore, we strongly advise against plunging into a sea of data in order to find out something that, currently, is neitherdefined nor definable without a business case and aware pilot tests.

Notwithstanding, the concept of BD has spread fast, and firms should carefully figure out if they want and are able to keep thepace with it, and they should clarify the consequences, both positive and negative, of framing BD within their processes and stra-tegies. For instance, according to Tambe (2014), investments in traditional database systems may remain more effective if managersdo not want to face risks and costs for attracting the BD expertise. Waiting can be a reasonable strategy, keeping in mind that bothcosts of acquiring the needed skills, and the benefits achievable through a BD project should decrease over time.

5.3. Limitations and suggestions for further research

As is often the case, also this work suffers from some limitations. First, although it relies on a sound scientific base, this manuscriptis conceptual in nature: a BD-enabled approach would require a further refinement and conceptualization of how BD could directly orindirectly affect the CRM inputs and outputs explicitly, for instance by formulating specific hypotheses. Second, the word treemethodology we used for the content analysis is still little applied for scientific purposes, and literature does not provide anyguideline for its development. Thus, if on one side the word tree protocol we built might be considered as a reference for futureapplications, since it relies on both common sense and a reasonable logic, on the other side it should be extended and formalized forstrengthening its validity.

Despite these limitations, this work sketches out the BD-enabled CRM concept that could be worth of further research. Therefore,as BD literature strongly lacks empirical works, we suggest three possible future developments. First, it would be interesting to assessthe effect of BD initiatives on CRM CSFs through a case study approach that, according to Yin (2014), would be a suitable exploratoryresearch method in this context. Second, the insights emerging from one or more exploratory case studies could support formulationand test of specific hypotheses, laid on the propositions we presented, about the relationship between BD initiatives and CRMenabling factors to be tested. Alternatively, a third potential development is to replicate and extend this study into other contexts,different from the CRM one, in order to evaluate the generalizability of our statements about BD, and to evaluate if similar con-clusions may be drawn.


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Appendix A

Table A.1References of the literature review.

Dimension of classification ReferencesCSF (citations out of total retained papers)

Strategic planning of CRM

CRM strategy (40) Ahearne, Rapp, Mariadoss, and Ganesan (2012); Allen and Mcmenamin (2003); Anderson, Jolly, andFairhurst (2007); Batenburg and Versendaal (2007); Bohling et al. (2006); Boulding, Staelin, Ehret, andJohnston (2005); Bygstad (2003); Chalmeta (2006); Diffley and McCole (2015); Dimitriadis and Stevens(2008); Eichorn (2004); Eid (2007); Finnegan and Currie (2010); Frow, Payne, Wilkinson, and Young(2011); Kale (2004); Keramati, Samadi, Nazari-Shirkouhi, and Askari (2012); Kim (2004); Kivetz andSimonson (2003); Landry, Arnold, and Arndt (2005); Lee & Park (2005); Mendoza et al. (2007); Nguyenand Mutum (2012); Nguyen, Sherif, and Newby (2007); Padmanabhan and Tuzhilin (2003); Pan & Lee(2003); Payne and Frow (2004, 2005); Peelen, van Montfort, Beltman, and Klerkx (2009); Rapp,Trainor, and Agnihotri (2010); Rigby and Ledingham (2004); Roh, Ahn, and Han (2005); Ryals (2005);Sin, Tse, and Yim (2005); Steel, Dubelaar, and Ewing (2013); Tamošiūniene and Jasilioniene (2007);Tanner, Ahearne, Leigh, Mason, and Moncrief (2005); Vazifehdust, Shahnavazi, Reza, Jourshari, andSharifi (2012); Verhoef (2003); Woodcock et al. (2011); Zablah et al. (2004a);

CRM vision / Firm's orientation towards customer (32) Boulding et al. (2005); Chalmeta (2006); Chang (2007); Coltman (2007); Da Silva and Rahimi (2007);Eid (2007); Garrido-Moreno and Padilla-Meléndez (2011); Garrido-Moreno, Lockett, and Garcia-Morales (2015); Jan and Abdullah (2014); Javalgi, Radulovich, Pendleton, and Scherer (2005);Jayachandran, Sharma, Kaufman, and Raman (2005); Kale (2004); Keramati et al. (2012); King andBurgess (2008); Lüneborg and Nielsen (2003); Nguyen and Mutum (2012); Pan and Lee (2003); Peelenet al. (2009); Raman, Wittmann, and Rauseo (2006); Rapp et al. (2010); Reinartz et al. (2004); Sen andSinha (2011); Shang and Lin (2010); Sin et al. (2005); Steel et al. (2013); Teo, Devadoss, and Pan(2006); Trainor et al. (2014); Vazifehdust et al. (2012); Wang & Feng (2012); Woodcock et al. (2011);Zablah et al. (2004a), Zablah et al. (2004a, 2004b);

Evaluation and monitoring (23) Anderson et al. (2007); Boulding et al. (2005); Chalmeta (2006); Cooper, Gwin, and Wakefield (2008);Curry and Kkolou (2004); Da Silva and Rahimi (2007); Eid (2007); Frow et al. (2011); Garrido-Moreno& Padilla-Meléndez (2011); Keramati et al. (2012); Malthouse et al. (2013); Mendoza et al. (2007);Mishra and Mishra (2009); Nguyen et al. (2007); Pan and Lee (2003); Payne and Frow (2005); Peelenet al. (2009); Rigby and Ledingham (2004); Shanks, Jagielska, and Jayaganesh (2009); Vazifehdust et al.(2012); Woodcock et al. (2011); Zablah et al. (2004a), Zablah et al. (2004a, 2004b);

Context (5) Dimitriadis and Stevens (2008); Kim, Park, Dubinsky, and Chaiy (2012); Ramaseshan, Bejou, Jain,Mason, and Pancras (2006); Sharma and Iyer (2007); Steel et al. (2013);

Infrastructure for IT in CRMInformation Technology (44) Ahn, Kim, and Han (2003); Anderson et al. (2007); Avlonitis and Panagopoulos (2005); Becker, Greve,

and Albers (2009); Chang (2007); Croteau and Li (2003); Curry and Kkolou (2004); Da Silva and Rahimi(2007); Eid (2007); Ferguson, Lin, and Chen (2004); Garrido-Moreno and Padilla-Meléndez (2011);Garrido-Moreno, Lockett, and García-Morales (2014, 2015); Harrigan, Ramsey, and Ibbotson (2011);Hasanian, Chong, and Gan (2015); Jan and Abdullah (2014); Javalgi et al. (2005); Keramati et al.(2012); Kim (2004); Kim and Pan (2006); King and Burgess (2008); Landry et al. (2005); Lindgreen andAntioco (2005); Mack et al. (2005); Malthouse et al. (2013); Mendoza et al. (2007); Mishra and Mishra(2009); Osarenkhoe and Bennani (2007); Pan and Lee (2003); Payne and Frow (2005); Rapp et al.(2010); Roh et al. (2005); Salomann, Dous, Kolbe, and Brenner (2005); Shum et al. (2008); Sin et al.(2005); Teo et al. (2006); Trainor et al. (2014); Tuzhilin (2012); Vazifehdust et al. (2012); Vella andCaruana (2012); Wang & Feng (2012); Woodcock et al. (2011); Yim, Anderson, and Swaminathan(2004); Zablah et al. (2004a);

Insight and data management in CRMKnowledge management (19) Arnett and Badrinarayanan (2005); Croteau and Li (2003); du Plessis and Boon (2004); Garrido-Moreno

and Padilla-Meléndez (2011); Garrido-Moreno et al. (2014, 2015); Hasanian et al. (2015); Jordan(2003); King and Burgess (2008); Lin, Su, and Chien (2006); Osarenkhoe and Bennani (2007); Salomannet al. (2005); Shang and Lin (2010); Sin et al. (2005); Tanner et al. (2005); Vazifehdust et al. (2012); Xuand Walton (2005); Yim et al. (2004); Zablah et al. (2004a);

Customer data management (28) Anderson et al. (2007); Campbell (2003); Chalmeta (2006); Chang (2007); Dimitriadis and Stevens(2008); Eichorn (2004); Frow et al. (2011); Harrigan et al. (2011); Jan and Abdullah (2014); Kale(2004); Keramati et al. (2012); Kim (2004); Landry et al. (2005); Lindgreen & Antioco (2005); Mendozaet al. (2007); Mishra and Mishra (2009); Öztaysi, Sezgin, and Özok (2011); Padmanabhan and Tuzhilin(2003); Pan & Lee (2003); Payne and Frow (2005); Roh et al. (2005); Salomann et al. (2005); Sin et al.(2005); Tanner et al. (2005); Teo et al. (2006); Tuzhilin (2012); Vazifehdust et al. (2012); Zablah et al.(2004b);

CRM projectPresence of a CRM champion (9) Allen and Mcmenamin (2003); Garrido-Moreno et al. (2014); Kim (2004); Kim and Pan (2006); Saini,

Grewal, and Johnson (2010); Steel et al. (2013); Teo et al. (2006); Vazifehdust et al. (2012); Zablahet al. (2004b);

(continued on next page)


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Table A.1 (continued)

Dimension of classification ReferencesCSF (citations out of total retained papers)

Leadership / commitment from top management (35) Allen and Mcmenamin (2003); Bohling et al. (2006); Campbell (2003); Chalmeta (2006); Chang (2007);Croteau and Li (2003); Curry and Kkolou (2004); Da Silva and Rahimi (2007); Eid (2007); Garrido-Moreno and Padilla-Meléndez (2011); Garrido-Moreno et al. (2014, 2015); Kale (2004); Keramati et al.(2012); Kim and Pan (2006); King and Burgess (2008); Malthouse et al. (2013); Mendoza et al. (2007);Mishra and Mishra (2009); Osarenkhoe and Bennani (2007); Pan and Lee (2003); Papadopoulos, Ojiako,Chipulu, and Lee (2012); Peelen et al. (2009); Plakoyiannaki, Tzokas, Dimitratos, and Saren (2008);Rigby and Ledingham (2004); Roh et al. (2005); Saini et al. (2010); Sen and Sinha (2011); Shum et al.(2008); Tamošiūniene and Jasilioniene (2007); Teo et al. (2006); Vazifehdust et al. (2012); Woodcocket al. (2011); Xu and Walton (2005); Zablah et al. (2004b);

Project management (18) Anderson et al. (2007); Bohling et al. (2006); Bygstad (2003); Chalmeta (2006); Curry and Kkolou(2004); Da Silva and Rahimi (2007); Dimitriadis and Stevens (2008); Eid (2007); Kim (2004); Kim andPan (2006); Maklan, Knox, and Ryals (2005); Mendoza et al. (2007); Mishra and Mishra (2009);Papadopoulos et al. (2012); Plouffe, Williams, and Leigh (2004); Rahimi and Berman (2009); Wilson,Clark, and Smith (2007); Zablah et al. (2004b);

Change management (17) Bohling et al. (2006); Bygstad (2003); Chalmeta (2006); Dimitriadis and Stevens (2008); Eid (2007);Kale (2004); Keramati et al. (2012); Kim (2004); Kim and Pan (2006); Landry et al. (2005); Mishra andMishra (2009); Rigby and Ledingham (2004); Salomann et al. (2005); Sen and Sinha (2011);Tamošiūniene and Jasilioniene (2007); Woodcock et al. (2011); Zablah et al. (2004b);

Employees' involvement (16) Bygstad (2003); Campbell (2003); Chalmeta (2006); Chang (2007); Garrido-Moreno et al. (2014, 2015);Kim (2004); Kim and Pan (2006); Mendoza et al. (2007); Mishra and Mishra (2009); Pass, Evans andSchlacter (2004); Shang and Lin (2010); Shum et al. (2008); Vazifehdust et al. (2012); Yim et al. (2004);Zablah et al. (2004b);

Education and training (25) Campbell (2003); Chalmeta (2006); Chang (2007); Curry and Kkolou (2004); Dimitriadis and Stevens(2008); Eid (2007); Garrido-Moreno and Padilla-Meléndez (2011); Garrido-Moreno et al. (2014, 2015);Kale (2004); Kim (2004); Kim, Kim, and Park (2010); Landry et al. (2005); Lindgreen & Antioco (2005);Osarenkhoe and Bennani (2007); Papadopoulos et al. (2012); Raman et al. (2006); Saini et al. (2010);Shang & Lin (2010); Shum et al. (2008); Sigala (2005); Vazifehdust et al. (2012); Woodcock et al.(2011); Yim et al. (2004); Zablah et al. (2004b);

Endowment of resources (15) Anderson et al. (2007); Curry and Kkolou (2004); Da Silva and Rahimi (2007); Diffley and McCole(2015); Keramati et al. (2012); Kim and Pan (2006); Malthouse et al. (2013); Papadopoulos et al.(2012); Roh et al. (2005); Saini et al. (2010); Sigala (2005); Sin et al. (2005); Vazifehdust et al. (2012);Zablah et al. (2004a, 2004b);

CRM and organizational impact

IT-processes alignment / Task-Technology Fit (17) Allen and Mcmenamin (2003); Brohman et al. (2003); Campbell (2003); Javalgi et al. (2005); Kim(2004); Nguyen and Mutum (2012); Pan and Lee (2003); Papadopoulos et al. (2012); Raman et al.(2006); Rapp et al. (2010); Rigby and Ledingham (2004); Roh et al. (2005); Sen and Sinha (2011);Shanks et al. (2009); Tamošiūniene and Jasilioniene (2007); Woodcock et al. (2011); Zablah et al.(2004b);

BPR (20) Allen and Mcmenamin (2003); Bohling et al. (2006); Boulding et al. (2005); Bygstad (2003); Chalmeta(2006); Chang (2007); Dimitriadis and Stevens (2008); Ferguson et al. (2004); Kale (2004); King andBurgess (2008); Landry et al. (2005); Lindgreen & Antioco (2005); Mishra and Mishra (2009); Ramanet al. (2006); Ramaseshan et al. (2006); Rigby and Ledingham (2004); Tamošiūniene and Jasilioniene(2007); Teo et al. (2006); Trainor et al. (2014); Zablah et al. (2004b);

Organizational culture and structure (38) Becker et al. (2009); Bygstad (2003); Chang (2007); Curry and Kkolou (2004); Diffley and McCole(2015); Dimitriadis and Stevens (2008); Eichorn (2004); Eid (2007); Garrido-Moreno and Padilla-Meléndez (2011); Hart, Hogg, and Banerjee (2004); Hasanian et al. (2015); Iriana, Buttle, and Ang(2013); Jayachandran et al. (2005); Keramati et al. (2012); King and Burgess (2008); Mack, Mayo, andKhare (2005); Malthouse et al. (2013); Mishra and Mishra (2009); Nguyen and Mutum (2012); Ramanet al. (2006); Ramaseshan et al. (2006); Reinartz et al. (2004); Roh et al. (2005); Shang and Lin (2010);Shum et al. (2008); Sigala (2005); Sin et al. (2005); Steel et al. (2013); Tamošiūniene and Jasilioniene(2007); Tanner et al. (2005); Trainor et al. (2014); Vazifehdust et al. (2012); Wang & Feng (2012);Wilcox and Gurǎu (2003); Woodcock et al. (2011); Xu and Walton (2005); Yim et al. (2004);

Inter-functional collaboration / communication /integration (17)

Arnett and Badrinarayanan (2005); Boulding et al. (2005); Bygstad (2003); Campbell (2003); Chang(2007); Cooper et al. (2008); Curry and Kkolou (2004); Da Silva and Rahimi (2007); Eichorn (2004);Garrido-Moreno and Padilla-Meléndez (2011); King and Burgess (2008); Mack et al. (2005); Mendozaet al. (2007); Osarenkhoe & Bennani (2007); Shum et al. (2008); Yim et al. (2004); Zablah et al. (2004b);

Reward system (16) Campbell (2003); Chang (2007); Cooper et al. (2008); Dimitriadis & Stevens (2008); Garrido-Morenoand Padilla-Meléndez (2011); Garrido-Moreno et al. (2014, 2015); Keramati et al. (2012); Kim et al.(2010); Landry et al. (2005); Reinartz et al. (2004); Shang and Lin (2010); Sigala (2005); Wang and Feng(2012); Yim et al. (2004); Zablah et al. (2004b);

Experience (5) Allen and Mcmenamin (2003); Garrido-Moreno and Padilla-Meléndez (2011); Hart et al. (2004);Srinivasan and Moorman (2005); Steel et al. (2013);


16

Appendix B

Fig. B.1. Word tree of the keyword Strategy.


17

Fig. B.2. Word tree of the keyword Needs.


18

Fig. B.3. Word tree of the keyword Technology.


19

Fig. B.4. Word tree of the keyword Value.

Fig. B.5. Word tree of the keyword Integration.


20

Fig. B.6. Word tree of the keyword Knowledge.


21

Fig. B.7. Word tree of the keyword Management.


22

Fig. B.8. Word tree of the keyword Resources.


23

Fig. B.9. Word tree of the keyword Structure.


24

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